YOLO-Banana: An Effective Grading Method for Banana Appearance Quality

Abstract

The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits. In this paper, we propose an improved YOLOv5-based model, YOLO-Banana, to effectively grade banana appearance quality based on the number of banana defect points. Due to the minor and dense defects on the surface of bananas, existing detection algorithms have poor detection results and high missing rates. To address this, we propose a density-based spatial clustering of applications with noise (DBSCAN) and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values, thereby enhancing the network’s recognition accuracy. Moreover, the optimized progressive aggregated network (PANet) enables better multi-level feature fusion. Additionally, the non-maximum suppression function is replaced with a weighted non-maximum suppression (weighted NMS) function based on distance intersection over union (DIoU). Experimental results show that the model’s accuracy is improved by 2.3% compared to the original YOLOv5 network model, thereby effectively grading the banana appearance quality.

Keywords

YOLOv5 / banana appearance grading / clustering algorithm / weighted non-maximum suppression (weighted NMS) / progressive aggregated network (PANet)